Univariate data manipulation and plots
Nick Toimieri
sys.date()
1 Triggers and Settings
2 Import fish and manipulate data a bit
Minimum vis for fish analysis = 2-m
Kelp depths = 5 -m Fish depths = 5, 10 -m Inv depths = 5 -m
Visibility data for 2015 are fake. DI was bad and set at 1.0 m; other sites set at 3.0 m
3 FISH
3.1 Rockfish YOY
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
## Joining, by = "taxa"
3.2 Fish
Fish depth = 5, 10
## Joining, by = "taxa"
3.3 Fish and YOY facet by site x year
## Joining, by = "taxa"
## Joining, by = "taxa"
4 ALGAE
Kelp depth = 5
## `summarise()` has grouped output by 'year', 'site', 'transect', 'species',
## 'zone', 'area'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'species', 'zone', 'area'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'spp'. You can override
## using the `.groups` argument.4.1 Big three: Nero, Macro, Ptero
Plot the big 3 species: Macro, Nero, Ptero
4.1.1 Coastwide
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
5 INVERTEBRATES
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'species', 'zone', 'area', 'taxa'. You can override using the `.groups`
## argument.
## `summarise()` has grouped output by 'year', 'site', 'species', 'zone', 'area'.
## You can override using the `.groups` argument.## # A tibble: 6 × 2
## year no
## <dbl> <dbl>
## 1 2015 0
## 2 2016 4
## 3 2017 0
## 4 2018 3
## 5 2019 1
## 6 2021 05.1 Summed Urchins
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'zone', 'area'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone', 'area'. You can
## override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'area', 'zone'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
Max total urchin density at Tatoosh
5-m = 4.0503465 m^-2 10-m = 10.0752194 m^-2
5.2 Urchins by species
Probably worth dropping into the supplement to show that it was mostly purple urchins. Partly interesting because there is no purple urchin fishery, but there is a red one. I think.
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'zone', 'area'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
Max total urchin density at Tatoosh
5-m = 3.7336798 m^-2 10-m = 9.5052194 m^-2
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
Purple urchin density in 2015 = 0.0116667 urchins per m^2 Purple urchin density in 2015 = 1.7637508 urchins per m^2
Increase was 151.1786432 fold.
Purple urchin density in 2015 = 0.7299487 urchins per m^2
Density compared to 2015 was 62.5670286 fold.
5.3 Seastars
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'area', 'zone'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
## `summarise()` has grouped output by 'site', 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
Ugly..no aliby..
## `summarise()` has grouped output by 'year', 'site', 'transect', 'observer',
## 'zone', 'area'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'year', 'site', 'zone'. You can override
## using the `.groups` argument.
6 Output data for multivariate statistics
6.1 Combine kelp, fish, and inverts for capscale analysis
Not yet transformed to wide format in order to retain SD & SE info upon saving out. Transform in the Multiariate analysis rmd.
Output data files to use for multivariate ordinations.
Use these files and not raw files. Kelp and invert data have been converted to density to account for different transect lengths etc.
## # A tibble: 310 × 33
## # Groups: site, year, area, zone, transect [310]
## year site trans…¹ area zone AUFL BAIT CHNU COTT EMBI GOBI HEXA
## <dbl> <fct> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016 Destruct… 1 N 10 0 0 0 0 0 0 1
## 2 2016 Destruct… 2 N 10 0 0 0 0 0 0 3
## 3 2016 Destruct… 3 N 10 0 0 0 0 0 0 1
## 4 2017 Destruct… 1 N 5 0 0 0 0 13 0 1
## 5 2017 Destruct… 4 N 5 0 0 0 0 2 0 1
## 6 2017 Destruct… 1 S 5 0 0 0 0 1 0 0
## 7 2017 Destruct… 2 S 5 0 0 0 0 4 0 1
## 8 2019 Destruct… 1 N 5 0 0 0 0 2 0 6
## 9 2019 Destruct… 2 N 5 0 0 0 0 0 0 14
## 10 2019 Destruct… 3 N 5 0 0 0 0 6 0 9
## # … with 300 more rows, 21 more variables: OPEL <dbl>, PHOL <dbl>, RIMU <dbl>,
## # RYOY <dbl>, SCMA <dbl>, SEBYTyoy <dbl>, SECA <dbl>, SECAyoy <dbl>,
## # SEFL <dbl>, SEMA <dbl>, SEMAyoy <dbl>, SEME <dbl>, SEMY <dbl>,
## # SEMYyoy <dbl>, SENE <dbl>, SENEyoy <dbl>, SEPI <dbl>, SEPIyoy <dbl>,
## # TOTyoy <dbl>, UNID <dbl>, SEBYT <dbl>, and abbreviated variable name
## # ¹transect## # A tibble: 310 × 18
## # Groups: year, site, transect, zone, area [310]
## year site trans…¹ area zone AGAFIM ALAMAR COSCOS CYMTRI DESSPP LAMSET
## <dbl> <fct> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 Neah Bay 1 D 5 0 0 0 0 0 0
## 2 2015 Neah Bay 2 D 5 0 0 0 0 0 0
## 3 2015 Tatoosh … 1 D 5 0 0 0 0 0 0
## 4 2015 Tatoosh … 2 D 5 0 0 0 0 0 0
## 5 2015 Cape Ala… 1 D 5 0 0 0 0 0 0
## 6 2015 Cape Ala… 2 D 5 0 0 0 0 0 0
## 7 2015 Cape Joh… 1 D 5 0 0 0 0 0 0
## 8 2015 Cape Joh… 2 D 5 0 0 0 0 0 0
## 9 2015 Destruct… 1 D 5 0 0 0.133 0 0 0
## 10 2015 Destruct… 2 D 5 0 0 0.025 0 0.0167 0.0167
## # … with 300 more rows, 7 more variables: MACPYR <dbl>, NERLUE <dbl>,
## # NO_ALG <dbl>, PLEGAR <dbl>, PTECAL <dbl>, SACGRO <dbl>, SACLAT <dbl>, and
## # abbreviated variable name ¹transect## # A tibble: 310 × 30
## # Groups: year, site, transect, zone, area [310]
## year site trans…¹ area zone anemone barna…² bivalve blood…³ brood…⁴
## <dbl> <fct> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2015 Neah Bay 1 D 5 0 0 0 0.183 0
## 2 2015 Neah Bay 2 D 5 0 0 0 0.167 0
## 3 2015 Tatoosh Is… 1 D 5 0.00625 0 0.00556 0.05 0
## 4 2015 Tatoosh Is… 2 D 5 0.0125 0 0.0167 0.05 0
## 5 2015 Cape Alava 1 D 5 0 0 0.00370 0 0
## 6 2015 Cape Alava 2 D 5 0 0 0 0.0667 0
## 7 2015 Cape Johns… 1 D 5 0 0 0 0 0
## 8 2015 Cape Johns… 2 D 5 0.00208 0 0.00556 0 0
## 9 2015 Destructio… 1 D 5 0 0 0 0.0333 0
## 10 2015 Destructio… 2 D 5 0.00833 0 0 0.167 0
## # … with 300 more rows, 20 more variables: chiton <dbl>, crabs <dbl>,
## # cucumber <dbl>, `green urchin` <dbl>, hermit_crabs <dbl>,
## # `kelp crab` <dbl>, `large star` <dbl>, `leather star` <dbl>,
## # `medium star` <dbl>, nudibranch <dbl>, Pisaster <dbl>,
## # `purple urchin` <dbl>, Pycnopodia <dbl>, `red urchin` <dbl>,
## # sea_star_YOY <dbl>, `shelled gastropod` <dbl>, `shelled mussel` <dbl>,
## # sponge <dbl>, tunicate <dbl>, `NA` <dbl>, and abbreviated variable names …## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.## # A tibble: 30 × 5
## # Groups: Year [6]
## Year Site Fish Kelp Invertebrates
## <dbl> <fct> <dbl> <dbl> <dbl>
## 1 2015 Cape Johnson 2 2 2
## 2 2015 Cape Alava 2 2 2
## 3 2015 Tatoosh Island 2 2 2
## 4 2015 Neah Bay 2 2 2
## 5 2015 Destruction Island NA 2 2
## 6 2016 Destruction Island 3 7 7
## 7 2016 Cape Johnson 10 13 13
## 8 2016 Cape Alava 12 10 10
## 9 2016 Tatoosh Island 8 9 9
## 10 2016 Neah Bay 10 10 10
## # … with 20 more rows6.2
combind fish and kelp in wide format using fish COUNT data. include area info for fish and kelp
## `summarise()` has grouped output by 'site', 'year', 'zone', 'area', 'transect'.
## You can override using the `.groups` argument.
## `summarise()` has grouped output by 'site', 'year', 'zone', 'area'. You can
## override using the `.groups` argument.## [1] 82 32## [1] 4023## `summarise()` has grouped output by 'site', 'year', 'area', 'zone'. You can
## override using the `.groups` argument.## [1] 100 18## Joining, by = c("site", "year", "zone", "area")## [1] 82 467 Quick view of what species to include in kelp ordinations. Basically only the big three have any abundance.
7.1 Algae
## `summarise()` has grouped output by 'species'. You can override using the
## `.groups` argument.
7.2 Fish
Note, the y-axes are very different
## `summarise()` has grouped output by 'taxa'. You can override using the
## `.groups` argument.
7.2.1 NOTES
for multivariate analysis; cut and paste to that file
fish.spp = c(“OPEL”, “HEXA”, “SECA”, “SCMA” ,“SENE”, “SEME”, ‘EMBI’,‘GOBI’)
yoy.spp = c(“SECAyoy”, “SEPIyoy”, “SEMYyoy”, “SEBYTyoy”,“RYOY”)
8 SOME USEFUL TABLES
## `summarise()` has grouped output by 'species'. You can override using the
## `.groups` argument.9 A tibble: 6 × 5
Species Common name size_class Group Total
## # A tibble: 6 × 3
## Species Density SD
## <chr> <dbl> <dbl>
## 1 Pterygophora californica 1.11 1.38
## 2 Nereocystis luetkeana 0.874 1.70
## 3 Macrocystis pyrifera 0.554 1.37
## 4 Laminaria setchellii 0.132 0.401
## 5 Saccharina dentigera 0.0877 0.288
## 6 Pleurophycus gardneri 0.0862 0.308## # A tibble: 6 × 4
## Species Taxa Density SD
## <chr> <chr> <dbl> <dbl>
## 1 Balanus nubilis barnacle 0.829 3.67
## 2 Strongylocentrotus purpuratus purple urchin 0.714 2.40
## 3 Nucella lamellosa shelled gastropod 0.293 1.36
## 4 Mesocentrotus franciscanus red urchin 0.160 0.600
## 5 Cucumaria miniata cucumber 0.107 0.159
## 6 Henricia leviuscula blood star 0.0960 0.091010 Kelp Canopy
Imports data file from other R file. Plot just kelp canopy. Add to ggarrange below.
## Scale for x is already present.
## Adding another scale for x, which will replace the existing scale.
Mean ha for 2004-2013: 700.6700019 ha +/- 43.6630069 s.e.
Total canopy 2014: 337.2999981 ha
Drop from previous decade: 0.4813964 % of previous cover.
Mean ha for 2015-2020:659.7333332 ha +/- 33.9434596 s.e.
Compared to earlier decade: 0.941575 % of previous cover.
Totals 2014 for Total, Nereo, Macro = 337.2999981, 89.6000009, 247.8999973 ha Totals 2020 for Total, Nereo, Macro = 701.8, 383.4, 318.6 ha
Change was 2.0806404, 4.2790178, 1.2851957 fold.
11 Combined Figures for MS
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.
12 Combined Kelp and Temperature figure
## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10"## `summarise()` has grouped output by 'year'. You can override using the
## `.groups` argument.
## Scale for x is already present. Adding another scale for x, which will replace
## the existing scale.
